Abhijeet A Rakshasbhuvankar, Lakshmi Nagarajan, Zhivko Zhelev, Shripada C Rao
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Detection of 'neonates with seizures' refers to the ability of the test to correctly identify a 'neonate' as 'seizure positive' or 'seizure negative' based on the detection of at least one seizure episode in the entire aEEG recording. Detection of 'individual seizures' refers to the ability of the test to correctly identify 'individual' seizure episodes within the same neonate rather than just diagnosing the neonate as 'seizure positive' or 'seizure negative'.</p><p><strong>Search methods: </strong>We searched CENTRAL, MEDLINE, Embase, clinical trials registries, and grey literature (Open Grey, Trove, and American Doctoral Dissertations) to 26 July 2022. We did not apply any language or publication status restrictions or any other filters.</p><p><strong>Selection criteria: </strong>We included prospective and retrospective studies investigating the accuracy of aEEG (index test) against the reference standard cEEG for the detection of neonatal seizures. To be eligible for inclusion, the studies must have compared aEEG with simultaneously recorded cEEG. There was no restriction on the number of leads, use of raw EEG traces, or experience and training of an aEEG interpreter. cEEG should have been recorded using at least nine electrodes and interpreted by a qualified person experienced in the interpretation of neonatal cEEG.</p><p><strong>Data collection and analysis: </strong>Working independently, two review authors collected data from the included studies in a prespecified form and assessed the quality of the included studies using the QUADAS-2 tool. For the outcome of 'neonates with seizures', we used a bivariate mixed-effects regression model to conduct a meta-analysis to derive pooled sensitivity, specificity, positive and negative likelihood ratios (LR), and their respective 95% confidence intervals (CI). We generated a summary receiver operating characteristic (SROC) curve to display the results of individual studies. We calculated post-test probabilities based on Bayes' theorem through Fagan nomograms. For the outcome of 'individual seizures', pooling of data was not possible because of the 'unit of analysis' issue. Instead, we performed a narrative synthesis. We assessed the certainty of the evidence using GRADE guidelines.</p><p><strong>Main results: </strong>We included 16 studies (562 infants) in the systematic review, of which only two studies interpreted the aEEGs prospectively at the bedside. Out of 16 studies, three studies (97 infants) described the accuracy of aEEG only for detecting 'infants with seizures', three studies (72 infants) described only 'individual seizures', while 10 studies (393 infants) described the accuracy of aEEG for detecting both. Ten of 16 studies were conducted in term and late preterm infants. Half of the included studies did not use raw EEG traces. Fourteen studies reported outcomes based only on retrospective interpretation. Ten of 16 studies used four electrodes (making this the most common approach amongst the included studies), and 10 studies' aEEG recordings exceeded six hours. Only two included studies used a seizure detection algorithm. In 14 studies, a neonatal or neurology consultant performed aEEG interpretation, and most (in 10 of 14 studies) had prior experience in aEEG interpretation. Accuracy of aEEG to diagnose 'neonates with seizures'. The only two prospective studies (53 participants) which interpreted aEEGs 'live' at the bedside, reported sensitivities of zero and 0.57 and specificities of 0.82 and 0.92, respectively. Meta-analysis of 13 studies (490 neonates) found that aEEG had a pooled sensitivity of 0.71 (95% CI 0.57 to 0.83), specificity of 0.84 (95% CI 0.59 to 0.95), positive LR of 4.50 (95% CI 1.55 to 13.04), and negative LR of 0.34 (95% CI 0.22 to 0.53) for the detection of 'neonates with seizures'. However, when we analysed only studies with low risk of bias (3 studies), sensitivity (0.56, 95% CI 0.02 to 0.99) and specificity (0.78, 95% CI 0.60 to 0.90) were even lower. There was significant statistical heterogeneity, which could not be explained based on threshold effect and exploratory analyses of forest plots. We graded the certainty of the evidence as low, in view of the high or unclear risk of bias in many studies, imprecision, and significant heterogeneity. Accuracy of aEEG to detect 'individual seizures' in neonates. The reported sensitivities of aEEG for the detection of 'individual seizures' ranged from 0 to 0.86 (13 studies, 465 neonates). We rated the certainty of the evidence as low. The common causes of missed seizures on aEEG (i.e. false negative) as reported by studies were short duration of seizures, localisation of seizures away from aEEG leads, low voltage, and inexperienced interpreter. The false-positive rates were high when interpreted live at the bedside and if the interpreters were inexperienced. Artefacts resulting from muscle movement, patting, hiccups, and insufficient electrode attachment were other common causes of false-positive results.</p><p><strong>Authors' conclusions: </strong>Low-certainty evidence suggests that aEEG has only moderate sensitivity and specificity for detecting 'neonates with seizures', and its ability to detect 'individual seizures' varies widely. These findings suggest that aEEG may not be sufficiently accurate for diagnosing neonatal seizures as it can under-diagnose or over-diagnose seizures. 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引用次数: 0
摘要
背景:常规视频脑电图(cEEG)是诊断和处理新生儿癫痫发作的参考标准。然而,大多数新生儿病房无法提供连续的床边脑电图服务。因此,一种相对简单的替代方法,称为振幅集成脑电图(aEEG),它使用有限数量的头皮电极,已经流行起来。aEEG允许对新生儿的脑电活动进行连续的床边监测。目的:本综述的主要目的是评估aEEG与参考标准cEEG检测“新生儿癫痫发作”和“个体癫痫发作”的准确性。“新生儿癫痫发作”的检测是指在整个aEEG记录中至少检测到一次癫痫发作的基础上,正确识别“新生儿”为“癫痫发作阳性”或“癫痫发作阴性”的能力。“个体癫痫发作”的检测是指该测试能够正确识别同一新生儿的“个体”癫痫发作,而不仅仅是诊断新生儿“癫痫发作阳性”或“癫痫发作阴性”。检索方法:我们检索了截至2022年7月26日的CENTRAL、MEDLINE、Embase、临床试验注册库和灰色文献(Open grey、Trove和American博士论文)。我们没有应用任何语言或出版状态限制或任何其他过滤器。选择标准:我们纳入前瞻性和回顾性研究,调查aEEG(指数测试)与参考标准cEEG检测新生儿癫痫发作的准确性。为了符合纳入条件,研究必须将aEEG与同时记录的cEEG进行比较。对引线的数量、原始脑电图的使用、aEEG口译员的经验和培训都没有限制。脑电图应至少使用9个电极记录,并由具有新生儿脑电图判读经验的合格人员判读。数据收集和分析:两位综述作者独立工作,以预先指定的形式收集纳入研究的数据,并使用QUADAS-2工具评估纳入研究的质量。对于“新生儿癫痫发作”的结果,我们使用双变量混合效应回归模型进行荟萃分析,以获得合并敏感性、特异性、阳性和阴性似然比(LR)及其各自的95%置信区间(CI)。我们生成了一个汇总的受试者工作特征(SROC)曲线来显示个体研究的结果。基于贝叶斯定理,通过Fagan模态图计算后验概率。对于“个体癫痫发作”的结果,由于“分析单元”的问题,数据池是不可能的。相反,我们进行了叙事合成。我们使用GRADE指南评估证据的确定性。主要结果:我们在系统综述中纳入了16项研究(562名婴儿),其中只有两项研究在床边前瞻性地解释了aEEGs。在16项研究中,3项研究(97名婴儿)描述了aEEG仅用于检测“婴儿癫痫发作”的准确性,3项研究(72名婴儿)描述了仅用于检测“个体癫痫发作”的准确性,而10项研究(393名婴儿)描述了aEEG用于检测两者的准确性。16项研究中有10项是在足月和晚期早产儿中进行的。其中一半的研究没有使用原始的脑电图记录。14项研究报告的结果仅基于回顾性解释。16项研究中有10项使用了4个电极(这是纳入研究中最常见的方法),10项研究的aEEG记录超过了6小时。只有两项纳入的研究使用了癫痫检测算法。在14项研究中,新生儿或神经病学顾问进行aEEG解释,并且大多数(14项研究中的10项)先前有aEEG解释经验。aEEG诊断“新生儿癫痫”的准确性。仅有的两项前瞻性研究(53名参与者)在床边解释了aEEGs的“活体”,报告的敏感性分别为0和0.57,特异性分别为0.82和0.92。对13项研究(490名新生儿)的荟萃分析发现,aEEG检测“新生儿癫痫发作”的总敏感性为0.71 (95% CI 0.57 ~ 0.83),特异性为0.84 (95% CI 0.59 ~ 0.95),阳性LR为4.50 (95% CI 1.55 ~ 13.04),阴性LR为0.34 (95% CI 0.22 ~ 0.53)。然而,当我们只分析低偏倚风险的研究(3项研究)时,敏感性(0.56,95% CI 0.02 ~ 0.99)和特异性(0.78,95% CI 0.60 ~ 0.90)甚至更低。在统计学上存在显著的异质性,不能用阈值效应和森林样地的探索性分析来解释。鉴于在许多研究中存在较高或不明确的偏倚风险、不精确和显著的异质性,我们将证据的确定性评级为低。aEEG检测新生儿“个体癫痫发作”的准确性。 已报道的aEEG检测“个体癫痫发作”的敏感性从0到0.86不等(13项研究,465名新生儿)。我们认为证据的可靠性很低。据研究报道,aEEG未发现癫痫发作(即假阴性)的常见原因是癫痫发作持续时间短,癫痫发作局部远离aEEG导联,低电压和缺乏经验的翻译。当现场口译和口译员缺乏经验时,假阳性率很高。由肌肉运动、拍打、打嗝和电极附着不足引起的伪影是假阳性结果的其他常见原因。作者的结论:低确定性的证据表明,aEEG在检测“新生儿癫痫发作”方面只有中等的敏感性和特异性,其检测“个体癫痫发作”的能力差异很大。这些发现表明,aEEG可能不足以准确诊断新生儿癫痫发作,因为它可能诊断不足或过度诊断癫痫发作。需要低偏倚风险的研究来明确地解决这个问题。
Amplitude-integrated electroencephalography compared with conventional video-electroencephalography for detection of neonatal seizures.
Background: Conventional video-electroencephalography (cEEG) is the reference standard for diagnosing and managing neonatal seizures. However, continuous bedside cEEG services are not available in most neonatal units. Hence, an alternative and relatively simple method called amplitude-integrated EEG (aEEG), which uses a limited number of scalp electrodes, has become popular. aEEG allows continuous bedside monitoring of the electrical activity of the brain in neonates.
Objectives: The primary objective of the review was to assess the accuracy of aEEG against the reference standard cEEG for the detection of 'neonates with seizures' and 'individual seizures'. Detection of 'neonates with seizures' refers to the ability of the test to correctly identify a 'neonate' as 'seizure positive' or 'seizure negative' based on the detection of at least one seizure episode in the entire aEEG recording. Detection of 'individual seizures' refers to the ability of the test to correctly identify 'individual' seizure episodes within the same neonate rather than just diagnosing the neonate as 'seizure positive' or 'seizure negative'.
Search methods: We searched CENTRAL, MEDLINE, Embase, clinical trials registries, and grey literature (Open Grey, Trove, and American Doctoral Dissertations) to 26 July 2022. We did not apply any language or publication status restrictions or any other filters.
Selection criteria: We included prospective and retrospective studies investigating the accuracy of aEEG (index test) against the reference standard cEEG for the detection of neonatal seizures. To be eligible for inclusion, the studies must have compared aEEG with simultaneously recorded cEEG. There was no restriction on the number of leads, use of raw EEG traces, or experience and training of an aEEG interpreter. cEEG should have been recorded using at least nine electrodes and interpreted by a qualified person experienced in the interpretation of neonatal cEEG.
Data collection and analysis: Working independently, two review authors collected data from the included studies in a prespecified form and assessed the quality of the included studies using the QUADAS-2 tool. For the outcome of 'neonates with seizures', we used a bivariate mixed-effects regression model to conduct a meta-analysis to derive pooled sensitivity, specificity, positive and negative likelihood ratios (LR), and their respective 95% confidence intervals (CI). We generated a summary receiver operating characteristic (SROC) curve to display the results of individual studies. We calculated post-test probabilities based on Bayes' theorem through Fagan nomograms. For the outcome of 'individual seizures', pooling of data was not possible because of the 'unit of analysis' issue. Instead, we performed a narrative synthesis. We assessed the certainty of the evidence using GRADE guidelines.
Main results: We included 16 studies (562 infants) in the systematic review, of which only two studies interpreted the aEEGs prospectively at the bedside. Out of 16 studies, three studies (97 infants) described the accuracy of aEEG only for detecting 'infants with seizures', three studies (72 infants) described only 'individual seizures', while 10 studies (393 infants) described the accuracy of aEEG for detecting both. Ten of 16 studies were conducted in term and late preterm infants. Half of the included studies did not use raw EEG traces. Fourteen studies reported outcomes based only on retrospective interpretation. Ten of 16 studies used four electrodes (making this the most common approach amongst the included studies), and 10 studies' aEEG recordings exceeded six hours. Only two included studies used a seizure detection algorithm. In 14 studies, a neonatal or neurology consultant performed aEEG interpretation, and most (in 10 of 14 studies) had prior experience in aEEG interpretation. Accuracy of aEEG to diagnose 'neonates with seizures'. The only two prospective studies (53 participants) which interpreted aEEGs 'live' at the bedside, reported sensitivities of zero and 0.57 and specificities of 0.82 and 0.92, respectively. Meta-analysis of 13 studies (490 neonates) found that aEEG had a pooled sensitivity of 0.71 (95% CI 0.57 to 0.83), specificity of 0.84 (95% CI 0.59 to 0.95), positive LR of 4.50 (95% CI 1.55 to 13.04), and negative LR of 0.34 (95% CI 0.22 to 0.53) for the detection of 'neonates with seizures'. However, when we analysed only studies with low risk of bias (3 studies), sensitivity (0.56, 95% CI 0.02 to 0.99) and specificity (0.78, 95% CI 0.60 to 0.90) were even lower. There was significant statistical heterogeneity, which could not be explained based on threshold effect and exploratory analyses of forest plots. We graded the certainty of the evidence as low, in view of the high or unclear risk of bias in many studies, imprecision, and significant heterogeneity. Accuracy of aEEG to detect 'individual seizures' in neonates. The reported sensitivities of aEEG for the detection of 'individual seizures' ranged from 0 to 0.86 (13 studies, 465 neonates). We rated the certainty of the evidence as low. The common causes of missed seizures on aEEG (i.e. false negative) as reported by studies were short duration of seizures, localisation of seizures away from aEEG leads, low voltage, and inexperienced interpreter. The false-positive rates were high when interpreted live at the bedside and if the interpreters were inexperienced. Artefacts resulting from muscle movement, patting, hiccups, and insufficient electrode attachment were other common causes of false-positive results.
Authors' conclusions: Low-certainty evidence suggests that aEEG has only moderate sensitivity and specificity for detecting 'neonates with seizures', and its ability to detect 'individual seizures' varies widely. These findings suggest that aEEG may not be sufficiently accurate for diagnosing neonatal seizures as it can under-diagnose or over-diagnose seizures. Studies with low risk of bias are needed to address the issue definitively.
期刊介绍:
The Cochrane Database of Systematic Reviews (CDSR) stands as the premier database for systematic reviews in healthcare. It comprises Cochrane Reviews, along with protocols for these reviews, editorials, and supplements. Owned and operated by Cochrane, a worldwide independent network of healthcare stakeholders, the CDSR (ISSN 1469-493X) encompasses a broad spectrum of health-related topics, including health services.